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5 changes: 5 additions & 0 deletions datafusion/functions/Cargo.toml
Original file line number Diff line number Diff line change
Expand Up @@ -320,3 +320,8 @@ required-features = ["math_expressions"]
harness = false
name = "floor_ceil"
required-features = ["math_expressions"]

[[bench]]
harness = false
name = "round"
required-features = ["math_expressions"]
154 changes: 154 additions & 0 deletions datafusion/functions/benches/round.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,154 @@
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.

extern crate criterion;

use arrow::datatypes::{DataType, Field, Float32Type, Float64Type};
use arrow::util::bench_util::create_primitive_array;
use criterion::{Criterion, SamplingMode, criterion_group, criterion_main};
use datafusion_common::ScalarValue;
use datafusion_common::config::ConfigOptions;
use datafusion_expr::{ColumnarValue, ScalarFunctionArgs};
use datafusion_functions::math::round;
use std::hint::black_box;
use std::sync::Arc;
use std::time::Duration;

fn criterion_benchmark(c: &mut Criterion) {
let round_fn = round();
let config_options = Arc::new(ConfigOptions::default());

for size in [1024, 4096, 8192] {
let mut group = c.benchmark_group(format!("round size={size}"));
group.sampling_mode(SamplingMode::Flat);
group.sample_size(10);
group.measurement_time(Duration::from_secs(10));

// Float64 array benchmark
let f64_array = Arc::new(create_primitive_array::<Float64Type>(size, 0.1));
let batch_len = f64_array.len();
let f64_args = vec![
ColumnarValue::Array(f64_array),
ColumnarValue::Scalar(ScalarValue::Int32(Some(2))),
];

group.bench_function("round_f64_array", |b| {
b.iter(|| {
let args_cloned = f64_args.clone();
black_box(
round_fn
.invoke_with_args(ScalarFunctionArgs {
args: args_cloned,
arg_fields: vec![
Field::new("a", DataType::Float64, true).into(),
Field::new("b", DataType::Int32, false).into(),
],
number_rows: batch_len,
return_field: Field::new("f", DataType::Float64, true).into(),
config_options: Arc::clone(&config_options),
})
.unwrap(),
)
})
});

// Float32 array benchmark
let f32_array = Arc::new(create_primitive_array::<Float32Type>(size, 0.1));
let f32_args = vec![
ColumnarValue::Array(f32_array),
ColumnarValue::Scalar(ScalarValue::Int32(Some(2))),
];

group.bench_function("round_f32_array", |b| {
b.iter(|| {
let args_cloned = f32_args.clone();
black_box(
round_fn
.invoke_with_args(ScalarFunctionArgs {
args: args_cloned,
arg_fields: vec![
Field::new("a", DataType::Float32, true).into(),
Field::new("b", DataType::Int32, false).into(),
],
number_rows: batch_len,
return_field: Field::new("f", DataType::Float32, true).into(),
config_options: Arc::clone(&config_options),
})
.unwrap(),
)
})
});

// Scalar benchmark (the optimization we added)
let scalar_f64_args = vec![
ColumnarValue::Scalar(ScalarValue::Float64(Some(std::f64::consts::PI))),
ColumnarValue::Scalar(ScalarValue::Int32(Some(2))),
];

group.bench_function("round_f64_scalar", |b| {
b.iter(|| {
let args_cloned = scalar_f64_args.clone();
black_box(
round_fn
.invoke_with_args(ScalarFunctionArgs {
args: args_cloned,
arg_fields: vec![
Field::new("a", DataType::Float64, false).into(),
Field::new("b", DataType::Int32, false).into(),
],
number_rows: 1,
return_field: Field::new("f", DataType::Float64, false)
.into(),
config_options: Arc::clone(&config_options),
})
.unwrap(),
)
})
});

let scalar_f32_args = vec![
ColumnarValue::Scalar(ScalarValue::Float32(Some(std::f32::consts::PI))),
ColumnarValue::Scalar(ScalarValue::Int32(Some(2))),
];

group.bench_function("round_f32_scalar", |b| {
b.iter(|| {
let args_cloned = scalar_f32_args.clone();
black_box(
round_fn
.invoke_with_args(ScalarFunctionArgs {
args: args_cloned,
arg_fields: vec![
Field::new("a", DataType::Float32, false).into(),
Field::new("b", DataType::Int32, false).into(),
],
number_rows: 1,
return_field: Field::new("f", DataType::Float32, false)
.into(),
config_options: Arc::clone(&config_options),
})
.unwrap(),
)
})
});

group.finish();
}
}

criterion_group!(benches, criterion_benchmark);
criterion_main!(benches);
44 changes: 44 additions & 0 deletions datafusion/functions/src/math/round.rs
Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,50 @@ impl ScalarUDFImpl for RoundFunc {
&default_decimal_places
};

// Scalar fast path for float types - avoid array conversion overhead
if let (ColumnarValue::Scalar(value_scalar), ColumnarValue::Scalar(dp_scalar)) =
(&args.args[0], decimal_places)
{
// Extract decimal places as i32
let dp = match dp_scalar {
ScalarValue::Int32(Some(dp)) => *dp,
ScalarValue::Int32(None) => {
return Ok(ColumnarValue::Scalar(ScalarValue::Float64(None)));
}
_ => {
// Fall through to array path for non-Int32 decimal places
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Is this arm possible in normal execution?

return round_columnar(
&args.args[0],
decimal_places,
args.number_rows,
);
}
};

match value_scalar {
ScalarValue::Float64(Some(v)) => {
let factor = 10_f64.powi(dp);
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I feel we should use round_float here for consistency?

return Ok(ColumnarValue::Scalar(ScalarValue::Float64(Some(
(v * factor).round() / factor,
))));
}
ScalarValue::Float64(None) => {
return Ok(ColumnarValue::Scalar(ScalarValue::Float64(None)));
}
ScalarValue::Float32(Some(v)) => {
let factor = 10_f32.powi(dp);
return Ok(ColumnarValue::Scalar(ScalarValue::Float32(Some(
(v * factor).round() / factor,
))));
}
ScalarValue::Float32(None) => {
return Ok(ColumnarValue::Scalar(ScalarValue::Float32(None)));
}
// For decimals and other types: fall through to array path
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Is there anything stopping us from supporting decimals as well?

_ => {}
}
}

round_columnar(&args.args[0], decimal_places, args.number_rows)
}

Expand Down